TY - DATA T1 - Predictive Modeling of Polymer-Derived Ceramics: Discovering Methods for the Design and Fabrication of Complex Disordered Solids PY - 2018/04/23 AU - Paul Rulis AU - Ridwan Sakidja AU - Michelle Paquette AU - Jinwoo Hwang AU - Nathan Oyler UR - https://figshare.com/articles/poster/Predictive_Modeling_of_Polymer-Derived_Ceramics_Discovering_Methods_for_the_Design_and_Fabrication_of_Complex_Disordered_Solids/6171164 DO - 10.6084/m9.figshare.6171164.v2 L4 - https://ndownloader.figshare.com/files/11169788 KW - NSF-SI2-2018 KW - Amorphous Materials Modeling KW - PECVD process KW - Fluctuation Electron Microscopy KW - Computational Physics KW - Condensed Matter Modelling and Density Functional Theory KW - Condensed Matter Physics N2 - This new project aims to develop a general, simulation-driven methodology for accurately recreating the atomic structure of substructure-containing amorphous solids and mapping resultant structures and properties back to fabrication conditions, ultimately enabling a computational design capability.The project combines state of the art computational techniques (AIMD, HRMC), modern optimization algorithms (e.g. artificial neural networks (ANNs), particle swarm optimization (PSO)), specialized experimental characterization techniques, (solid-state nuclear magnetic resonance (NMR), 4-dimensional scanning transmission electron microscopy (4D-STEM)), and advanced thin-film fabrication technology (plasma enhanced chemical vapor deposition (PECVD)). We will use a collection of thin-film amorphous preceramic polymers (a-BC:H, a-SiBCN:H, and a-SiCO:H) as suitably complex and technologically relevant case studies. ER -